"Day 35 of #100DaysOfPython: Working with Time and Calendar in Python"

🚀 **Day 34 of #100DaysOfPython – Date and Time in Python** 🕒 Python provides the **`datetime`** module to handle dates and times efficiently. Let’s explore the most useful features with simple examples 👇 --- ### 🧭 1️⃣ Importing the datetime Module ```python import datetime ``` This gives access to classes like `date`, `time`, `datetime`, and `timedelta`. --- ### 📅 2️⃣ Working with Dates ```python from datetime import date today = date.today() print("Today's date:", today) specific_date = date(2024, 2, 16) print("Specific date:", specific_date) ``` ✅ You can extract parts of a date: ```python print("Year:", today.year) print("Month:", today.month) print("Day:", today.day) ``` ✅ To find the weekday: ```python print("Weekday (0=Mon):", today.weekday()) print("ISO Weekday (1=Mon):", today.isoweekday()) ``` --- ### ⏰ 3️⃣ Working with Time ```python from datetime import time specific_time = time(14, 30, 15) print("Specific time:", specific_time) print("Hour:", specific_time.hour) print("Minute:", specific_time.minute) print("Second:", specific_time.second) ``` --- ### 📆 4️⃣ Date and Time Together ```python from datetime import datetime now = datetime.now() print("Current date and time:", now) specific_datetime = datetime(2024, 2, 16, 14, 30, 15) print("Specific date and time:", specific_datetime) ``` ✅ Extract individual parts: ```python print("Year:", now.year) print("Month:", now.month) print("Day:", now.day) print("Hour:", now.hour) print("Minute:", now.minute) print("Second:", now.second) ``` --- ### 🕓 5️⃣ Formatting Dates & Times ```python formatted_date = now.strftime("%Y-%m-%d") formatted_time = now.strftime("%H:%M:%S") formatted_datetime = now.strftime("%d-%b-%Y %I:%M %p") print("Formatted Date:", formatted_date) print("Formatted Time:", formatted_time) print("Formatted Date and Time:", formatted_datetime) ``` 📘 Example: `%Y` = Year, `%m` = Month, `%d` = Day, `%H` = Hour, `%M` = Minute, `%S` = Second, `%p` = AM/PM --- ### 🔁 6️⃣ Parsing Strings into Dates ```python from datetime import datetime date_string = "16-02-2024 14:30" parsed_date = datetime.strptime(date_string, "%d-%m-%Y %H:%M") print("Parsed Date and Time:", parsed_date) ``` --- ### ⏳ 7️⃣ Date and Time Arithmetic ```python from datetime import timedelta from datetime import date, datetime today = date.today() now = datetime.now() future_date = today + timedelta(days=7) past_date = today - timedelta(days=3) future_time = now + timedelta(hours=2) print("Date after 7 days:", future_date) print("Date 3 days ago:", past_date) print("Time after 2 hours:", future_time) ``` --- ✨ **In short:** - `date` → handles calendar dates - `time` → handles time only - `datetime` → combines both - `timedelta` → does date/time math 💬 What’s the most common date format you use in your projects? #Python #100DaysOfCode #PythonProgramming #LearningPython #DateTime

File Handling in Python: Saving Your Coffee Recipes 📁☕ Imagine creating the perfect coffee recipe today… but tomorrow you forget the steps! 😅 Wouldn’t it be great if you could save your recipes for later? That’s exactly what file handling does in Python - it helps you store and retrieve information whenever you want. 💾 🗂️ Reading & Writing Files In Python, you can easily create, write, and read files using the open() function. ✍️ Writing to a File file = open("recipes.txt", "w") # 'w' means write file.write("Latte = Coffee + Milk + Sugar\n") file.write("Espresso = Strong Coffee ☕\n") file.close() print("Recipes saved!") ✅ This creates a file called recipes.txt and writes inside it. 📖 Reading from a File file = open("recipes.txt", "r") # 'r' means read content = file.read() file.close() print("Your saved recipes:\n", content) ✅ Output: Your saved recipes: Latte = Coffee + Milk + Sugar Espresso = Strong Coffee ☕ 🔄 Modes in File Handling ->r - Read filew ->w - Write (overwrites old data) ->a - Append (adds new data) ->r+ - Read & write 🧠 Why It’s Important Saves data permanently Used in data analysis, web apps, and AI logs Helps automate real-world tasks Think of it as your digital recipe book - Python helps you write, store, and reuse your ideas anytime! 💡 💬 Try this today: Write a program that saves your favorite drink to a file and prints it back. #PythonWithKeshav #LearnPython #PythonBasics #FileHandling #CodingJourney #ProgrammingForBeginners #PythonForAll #Automation #STEMEducation #PythonLearning

I use Cursor agents and the IDE everyday. I started this summer and have not looked back. I've been a fan of VS Code, so the transition was easy for me (Cursor is a fork of VS code). I primarily use it for .ipynb files for research topics and python applications/.exe's. Couple things I've learned: Context and prompts are the fundamentals. Context for me means taking bits and pieces of code from stuff that already works and directing the agent towards that code. Prompting means taking the time to think and explain what I need via voice or typing (win+H, or Cursor’s voice feature). Context explainers in my prompts sometimes look like:  "𝘜𝘴𝘦 𝘭𝘪𝘯𝘦𝘴 2411-2512 𝘪𝘯 𝘦𝘹𝘢𝘮𝘱𝘭𝘦.𝘱𝘺 𝘵𝘰 𝘤𝘳𝘦𝘢𝘵𝘦 𝘢 𝘴𝘪𝘮𝘪𝘭𝘢𝘳 𝘦𝘭𝘦𝘮𝘦𝘯𝘵 𝘴𝘵𝘢𝘳𝘵𝘪𝘯𝘨 𝘢𝘵 𝘭𝘪𝘯𝘦 3456 𝘪𝘯 𝘢𝘱𝘱.𝘱𝘺" In my experience, the best predictor of success is if you understand what the agent needs to do in the code before letting it loose and are able to convey that information in the prompt. I think the human's prompt creation is similar to recognizing the type of problem in a Leetcode puzzle. There’s an aha moment: “Oh, this is a binary tree problem.” If you don’t recognize the type of problem and a path forward, there is absolutely no guarantee the agent will know (sometimes it does, sometimes it doesn’t) An example prompt thread from last week: “𝘜𝘴𝘦 𝘢 𝘱𝘺𝘵𝘩𝘰𝘯 𝘴𝘤𝘳𝘪𝘱𝘵 𝘵𝘰 𝘳𝘦𝘢𝘥 𝘵𝘩𝘦 @Tx_1_Rx_4_E_Field.csv 𝘱𝘩𝘢𝘴𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘌𝘻 𝘤𝘰𝘮𝘱𝘰𝘯𝘦𝘯𝘵 𝘢𝘯𝘥 𝘱𝘭𝘰𝘵 𝘪𝘵 𝘴𝘪𝘥𝘦 𝘣𝘺 𝘴𝘪𝘥𝘦 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘱𝘩𝘢𝘴𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘌𝘻 𝘢𝘧𝘵𝘦𝘳 𝘱𝘩𝘢𝘴𝘦 𝘶𝘯𝘸𝘳𝘢𝘱𝘱𝘪𝘯𝘨.” “𝘰𝘬 𝘨𝘳𝘦𝘢𝘵 𝘵𝘩𝘢𝘵 𝘸𝘰𝘳𝘬𝘦𝘥, 𝘯𝘰𝘸 𝘐 𝘩𝘢𝘷𝘦 𝘵𝘸𝘰 𝘴𝘦𝘵𝘴 𝘰𝘧 𝘥𝘢𝘵𝘢: 𝘛𝘩𝘦 𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭 @Tx_1_Rx_4_E_Field.csv 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 𝘵𝘩𝘦 1 m 𝘴𝘱𝘢𝘤𝘪𝘯𝘨 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 @Tx_1_Rx_2_E_Field.csv 𝘸𝘩𝘪𝘤𝘩 𝘪𝘴 2 m 𝘴𝘱𝘢𝘤𝘪𝘯𝘨. 𝘱𝘭𝘰𝘵 𝘵𝘩𝘦𝘴𝘦 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳 𝘪𝘯 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 1x2 𝘱𝘭𝘰𝘵, 𝘧𝘪𝘳𝘴𝘵 𝘵𝘩𝘦 𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭 𝘸𝘳𝘢𝘱𝘱𝘦𝘥 𝘢𝘯𝘥 𝘵𝘩𝘦𝘯 𝘵𝘩𝘦 𝘶𝘯𝘸𝘳𝘢𝘱𝘱𝘦𝘥.” “𝘰𝘬 𝘭𝘦𝘵𝘴 𝘥𝘰 𝘢 2x2 𝘱𝘭𝘰𝘵 𝘪𝘯𝘴𝘵𝘦𝘢𝘥 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘵𝘩𝘦 𝘹 𝘢𝘹𝘪𝘴 𝘪𝘴 𝘯𝘰𝘵 𝘲𝘶𝘪𝘵𝘦 𝘢𝘭𝘪𝘨𝘯𝘦𝘥.” This is a relatively simple example but this took maybe 2 minutes to generate and about 30 seconds for me to read and confirm it was applying numpy.unwrap to my data (which I could have explicitly mentioned). I sent this plot to a customer to explain why the phase data looked strange in Remcom's Wireless InSite simulation. The data simply needed to be unwrapped and sampled at a higher resolution. Now the time I would have taken to write code to read the .csv was spent on other more interesting tasks! Disclaimer: I am not sponsored by Cursor, but wish I was. #AI #Cursor #Agents #drama Remcom

#AI #ML #Python #Post4🚀 Decorator in Python, is a special function that modifies or enhances the behavior of another function or method — without permanently changing its code. You can think of a decorator as a wrapper that adds extra functionality around your existing functions. ------------------------------------------------------------- ✅️Real-World Use Cases ------------------------------------------------------------- 📌1. Logging def log(func): def wrapper(*args, **kwargs): print(f"Calling {func.__name__}") result = func(*args, **kwargs) print(f"{func.__name__} finished") return result return wrapper @log def add(a, b): return a + b add(2, 3) 📌2. Authorization (Flask/Django-style) def require_login(func): def wrapper(user): if not user.get("logged_in"): print("Access denied") return return func(user) return wrapper @require_login def view_profile(user): print(f"Welcome {user['name']}") view_profile({"name": "Alice", "logged_in": False}) 📌3. Timing Function Execution import time def timer(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) end = time.time() print(f"{func.__name__} took {end - start:.4f} seconds") return result return wrapper @timer def compute(): sum([i*i for i in range(100000)]) compute() Class-based decorators, which do the same thing as function-based ones — but give you more power and flexibility, especially when you need to store state or maintain configuration across multiple calls. ------------------------------------------------------------- ✅️What is a Class-Based Decorator? ------------------------------------------------------------- A class-based decorator is simply a class that: 📌1. Takes a function in its constructor (__init__) 📌2. Implements the __call__() method, so that the instance can be called like a function 👉This way, when Python sees @MyDecorator, it: 👉Creates an instance of MyDecorator, passing the decorated function to it. 👉Then, every time you call the decorated function, it actually calls MyDecorator.__call__(). ------------------------------------------------------------- ✍️Example 1: Simple Class Decorator ‐------------------------------------------------------------ class MyDecorator: def __init__(self, func): self.func = func # store the original function def __call__(self, *args, **kwargs): print("Before the function runs") result = self.func(*args, **kwargs) print("After the function runs") return result @MyDecorator def say_hello(): print("Hello!") say_hello() ------------------------------------------------------------- 💻Output: ------------------------------------------------------------- Before the function runs Hello! After the function runs

🚀 Just built my own Python data type using OOP & magic methods! We all know Python gives us built-in types like int, float, and list... But what if we could design our own — that behaves just like them? 🤯 That’s exactly what I did with PyMatrixEngine 🧠 I built a custom Matrix data type that supports operations such as: ➕ Addition (A + B) ➖ Subtraction (A - B) ✖️ Multiplication (A * B) 🔁 Transpose & Determinant All powered by Python’s magic methods (__add__, __mul__, __str__, and friends) 🪄 And here’s the cool part — If you input something that doesn’t form a valid matrix, this datatype automatically checks it and raises a clean, readable error. No more silent shape mismatches or confusing bugs ✅ You can simply drop the file in your project and start using it: from matrix import Matrix A = Matrix([[1,2],[3,4]]) B = Matrix([[5,6],[7,8]]) print(A + B) print(A * B) print(A.determinant()) It’s a fun deep-dive into Object-Oriented Programming (OOP) and Python’s hidden superpowers: magic methods ✨ 🧩 GitHub Repo → https://lnkd.in/gkrheMQS Would love to hear — what’s the coolest custom data type you’ve ever built in Python? #Python #OOP #MagicMethods #Coding #Matrix #Learning #PythonProjects #Developers #PythonTips

🚀 Day 42 of My Python Full Stack Journey 💡 Topic: NumPy Methods — The Powerhouse of Data Handling in Python! Today, I explored one of the most powerful libraries in Python — NumPy (Numerical Python). It’s the backbone of data science, AI, and scientific computing. Here’s what I learned and practiced 👇 🧩 1️⃣ Array Creation Methods np.array() → Creates arrays from Python lists np.zeros() → Creates arrays filled with zeros np.ones() → Creates arrays filled with ones np.arange() → Creates ranges of evenly spaced values np.linspace() → Generates evenly spaced numbers between a range np.full() → Fills an array with a constant value np.eye() → Creates an identity matrix np.random.rand() → Generates random floats between 0 and 1 np.random.randint() → Generates random integers np.empty() → Creates an uninitialized array (faster but filled with random memory values) 🧮 2️⃣ Array Attributes Every NumPy array comes with helpful attributes: .shape → Rows & columns .ndim → Number of dimensions .size → Total number of elements .dtype → Data type of elements 🔁 3️⃣ Reshaping & Flattening Transforming arrays is easy with NumPy: reshape() → Change array shape flatten() → Convert multi-dimensional to 1D ravel() → Similar to flatten, returns a view resize() → Change size (can repeat/truncate data) ➕ 4️⃣ Array Math Operations NumPy supports element-wise and matrix operations: np.add(), np.subtract(), np.multiply(), np.divide() np.dot() → Dot product np.power() → Exponentiation np.mod() → Modulus operation ⚙️ Code Example import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print(np.add(a, b)) print(np.reshape(np.arange(1, 7), (2, 3))) print(np.eye(3)) 💬 Key Takeaway NumPy makes data handling in Python blazing fast, memory-efficient, and easy to manipulate. It’s the foundation of Pandas, TensorFlow, and Scikit-learn — mastering it is essential for every developer! #Python #NumPy #PythonFullStack #10000Coders #LearningJourney #DataScience #MachineLearning #CodingCommunity #PythonDeveloper 10000 Coders Harish M Bhagavathula Srividya Spandana Chowdary

🧠 String Methods in Python — Master Text Manipulation Brought to you by programmingvalley.com Credit: @allinpython Strings are one of the most common data types in Python — and mastering them can make your code cleaner and faster. Here are some must-know string methods every developer should learn: → "HELlo".lower() → converts to lowercase → hello → "hello".upper() → converts to uppercase → HELLO → "hello world".capitalize() → makes first letter uppercase → Hello world → "hello world".title() → capitalizes each word → Hello World → " hello ".strip() → removes spaces → hello → "Hello".startswith("He") → checks if string starts with “He” → True → "Hello".endswith("lo") → checks if string ends with “lo” → True → "one,three".replace(",", "|") → replaces characters → one|three → "one,three".split(", ") → splits string → ['one', 'three'] → "-".join(["a", "b", "c"]) → joins list into string → a-b-c → "hello".find("e") → returns position → 1 → "hello".index("e") → similar to find() but raises error if not found → "hello world".count("o") → counts occurrences → 2 → "12345".isnumeric() → checks if all characters are numeric → True 🎓 Learn Python the right way: Python for Data Science → https://lnkd.in/d5iyumu4 Google IT Automation with Python → https://lnkd.in/dyJ4mYs9 Microsoft Python Development Certificate → https://lnkd.in/dDXX_AHM #Python #Coding #DataScience #LearnPython #ProgrammingValley #PythonTips

Daily Progress — Python + SQL Today, I practiced some simple but powerful Python basics: 🔹 input() function 🔹 Data types 🔹 String conversion and len() 🔹 Conditional statements (if-else) 🔹 String replacement using .replace() Python Practice: Age = 323 x = len(str(Age)) if x < 2:   print("Yes, it is") else:   print(x, "- No, it is not") phone_Num = "123-475-886" print(phone_Num.replace("-", "/")) And to make sure I don’t forget SQL while learning Python, I practiced one query today as well WITH CTE AS (  SELECT    s.Category,   SUM(s.Price * s.Quantity) AS Total_Revenue,   SUM(s.Quantity * p.Cost) AS Total_Cost  FROM Sales AS s  INNER JOIN Products AS p    ON s.Product_Name = p.Product_Name  GROUP BY s.Category ) SELECT   Category,  Total_Revenue,  Total_Cost,  (Total_Revenue - Total_Cost) AS Total_Profit FROM CTE ORDER BY Category, Total_Profit DESC; Lesson learned: Small consistent practice builds long-term mastery. #Python #SQL #DataAnalytics #LearningJourney #DataScience #AI #CodingEveryday #Consistency

🚀 Day 7 of #100DaysOfDataEngineering Topic: Python Advance - NumPy Basics Tags: #Python #NumPy #DataEngineering #DataScience Today marks the start of our journey into Python for numerical computing. Meet NumPy (Numerical Python), the core library that powers data transformations, mathematical operations, and many popular tools like Pandas and Scikit-learn. 💡 What is NumPy? NumPy provides multi-dimensional arrays (ndarray) and efficient functions to work with them. It is built for speed, allowing you to process large datasets much faster than standard Python lists. Install NumPy with: pip install numpy Import the library: import numpy as np 🧱 Creating Arrays You can create NumPy arrays directly from Python lists or nested lists: import numpy as np # 1D array arr1 = np.array([10, 20, 30]) # 2D array arr2 = np.array([[5, 10, 15], [20, 25, 30]]) print(arr1.shape) # (3,) print(arr2.shape) # (2, 3) print(arr1.dtype) # int64 ⚙️ Common Attributes AttributeDescriptionndarray.shapeDimensions of the array (rows, columns)ndarray.dtypeType of data stored (int, float, etc.)ndarray.ndimNumber of dimensionsndarray.reshape()Change array shape without changing data 📊 Built-in Methods NumPy includes several helpful functions for creating arrays quickly: # Evenly spaced values (like range) np.arange(0, 10, 2) # [0 2 4 6 8] # Arrays of zeros and ones np.zeros((2, 3)) # 2x3 array of zeros np.ones((3, 2)) # 3x2 array of ones # Equally spaced numbers np.linspace(0, 10, 5) # [0. 2.5 5. 7.5 10.] 🎲 Generating Random Numbers NumPy makes it simple to generate test data for experiments and simulations: # Random values (uniform distribution) np.random.rand(3, 4) # Random values (normal distribution) np.random.randn(2, 3) # Random integers between 4 and 40 np.random.randint(4, 40, 10) # 4x4 matrix of random integers up to 50 np.random.randint(50, size=(4,4)) ✅ Key Takeaway NumPy is all about speed and simplicity. It lets you handle large datasets and perform calculations efficiently. These array operations form the foundation of every scalable data pipeline.

Dictionaries in Python: The Pantry Labels of Your Code 🏷️ Your pantry has so many items — but how do you find things easily? You don’t just dump everything in boxes — you label them! “Sugar → 1kg” “Milk → 2 packets” “Coffee → 500g” That’s what Dictionaries in Python do - they store data as key–value pairs, just like labeled jars in your kitchen! 💡 What is a Dictionary? A dictionary helps you store and access data using names (keys) instead of positions. Think of it like this: Instead of saying “Give me the 3rd item”, you can say “Give me the sugar!” 📘 Example: pantry = { "Sugar": "1kg", "Milk": "2 packets", "Coffee": "500g" } Now, if you want to check what’s in your pantry: print(pantry) Output: {'Sugar': '1kg', 'Milk': '2 packets', 'Coffee': '500g'} 💬 Accessing Items You can get values by using their key names: print(pantry["Sugar"]) Output: 1kg Easy, right? No need to remember the position - just ask for the label! 🧠 Updating or Adding Items Refill something or add new stock: pantry["Sugar"] = "2kg" # update existing pantry["Tea"] = "1 box" # add new print(pantry) Output: {'Sugar': '2kg', 'Milk': '2 packets', 'Coffee': '500g', 'Tea': '1 box'} 💥 Removing Items You can remove an item once it’s finished: del pantry["Coffee"] print(pantry) Output: {'Sugar': '2kg', 'Milk': '2 packets', 'Tea': '1 box'} 💡 Why Dictionaries Are Powerful ✅ Easy to find data using names ✅ Data stays organized and readable ✅ Perfect for real-world applications - like storing user info, product details, etc. 🧠 Today’s takeaway: “Dictionaries are like labeled jars — they help you find exactly what you need without confusion!” 💬 Try this today: Create a dictionary with your favorite fruits and their colors 🍎 Then print each fruit with its color using: for fruit, color in fruits.items(): print(fruit, "is", color) #PythonWithKeshav #LearnPython #PythonBasics #CodingJourney #PythonDictionaries #ProgrammingForBeginners #PythonLearning #CodeSmart #STEMEducation #PythonForAll